Article Predicting the Potential Distribution of Paeonia veitchii (Paeoniaceae) in China by Incorporating Climate Change into a Maxent Model Keliang Zhang, Yin Zhang and Jun Tao * Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China; [email protected] (K.Z.); [email protected] (Y.Z.) * Correspondence: [email protected]; Tel.: +86-0514-8799-7219 Received: 14 January 2019; Accepted: 19 February 2019; Published: 20 February 2019 Abstract: A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. With the decrease of its population in recent decades, it has become a locally endangered species. In present study, we modeled the potential distribution of P. veitchii under current and future conditions, and evaluated the importance of the factors that shape its distribution. The results revealed a highly and moderately suitable habitat for P. veitchii that encompassed ca. 605,114 km2. The central area lies in northwest Sichuan Province. Elevation, temperature seasonality, annual mean precipitation, and precipitation seasonality were identified as the most important factors shaping the distribution of P. veitchii. Under the scenario with a low concentration of greenhouse gas emissions (RCP 2.6), we predicted an overall expansion of the potential distribution by 2050, followed by a slight contraction in 2070. However, with the scenario featuring intense greenhouse gas emissions (RCP 8.5), the range of suitable habitat should increase with the increasing intensity of global warming. The information that was obtained in the present study can provide background information related to the long-term conservation of this species. Keywords: bioclimatic; climate change; Maxent modeling; Paeonia veitchii; potential suitable habitat 1. Introduction Climate serves as one of the major factors that influence the geographical distribution of plant species as well as vegetation pattern and structure [1–3]. The 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) pointed out that the global climate has experienced a growing warming trend since the very beginning of the 20th century. Specifically, the average surface temperatures have increased by 0.85 ◦C worldwide from the late 1800 s to 2012; meanwhile, greenhouse gas emissions have dramatically increased. Levels of CO2, CH4, and N2O in the atmosphere have reached their highest concentrations in the last 800,000 years [3]. Climate change may lead to an increase in both temperatures and precipitation, which in turn can lead to increased plant growth rates. In addition, changes in the spatiotemporal climatic patterns have a major effect on plant recruitment, plant phenology, soil properties, plant diseases, pest management, and the properties of forest ecosystems [2,4,5]. If the effects of climate change on the future distribution of habitats for individual species can be predicted across the landscape, then this would help land managers to mitigate any potential threats to the habitats of those species [6]. This can guide the development of strategies that are related to resource development and use, and might include the preparation of germplasm and its storage [7,8]. Forests 2019, 10, 190; doi:10.3390/f10020190 www.mdpi.com/journal/forests Forests 2019, 10, 190 2 of 14 Species distribution modeling (SDM) can definitely help researchers to determine the ecological requirements of various species and predict the potential range of species based on ecology and biogeography; this is especially true when limited data are available [9]. At present, the most frequently used ecological models in the prediction of the distribution of various species are bioclimatic modeling (BIOCLIM) [10], domain environmental envelope (DOMAIN) [11], ecological niche factor analysis (ENFA) [12], Generalized Additive Model (GAM) [13], genetic algorithm for rule-set production (GARP) [14], and maximum entropy (Maxent) [15]. Among those models, Maxent modeling has been widely employed, because it performs well with either incomplete data or presence-only data [15]. Maxent modeling has been widely applied to plant and animal conservation [8,15], endangered species management [16], invasive species control [6,17], and in the regionalization of agriculture [4,6,16]. The Chinese have cultivated Paeonia, a monotypic genus in the Paeoniaceae, since the days of the Han Dynasty [18]. Known as the “king of flowers” and “flowers of richness and honor”, this genus represents the most popular garden plant in temperate regions of China [18,19]. Many wild peony species have been unsustainably harvested. Hong et al. [18] used past investigations and field surveys to review the conditions that affect wild peony species. Two species, P. ostii and P. cathayana, were found with populations of single isolated individuals. Several species, P. decomposita, P. qiui, P. rockii, and P. rotundiloba, have experienced excessive collection pressures. In addition, P. jishanensis and P. ludlowii are vulnerable due to human disturbance or urbanization. Without the rational delimitation of peony species and inadequate ecological and biological information, it will be impossible to work out a scientifically sound conservation strategy and practical conservation measures [18,20]. P. veitchii, an endemic species of China that occurs naturally in Gansu, Ningxia, Qinghai, Shaanxi, and Sichuan, in western China [21]. The root has been used in traditional medicines to treat convulsions [18]. The seeds contain unsaturated fatty acids, especially α-linolenic acid, an edible oil with promising health benefits [18]. In addition, it has served as the germplasm resource of cultivating a new variety of P. lactiflora. Previous studies have mainly focused on the extraction and analysis of chemical components [22], pollen fertility [23], and photosynthetic physiology [24]. However, little is known regarding the distribution of its habitat and the ecological factors that shape its suitable habitat. Whether the changing climate will affect the suitable habitat of P. veitchii is a crucial issue given the ecological and economic significance of the species, and enhancing our powers of prediction would contribute to management planning. Based on an extensive collection of geo-referenced occurrence records of P. veitchii and high-resolution environmental data for current and future climate scenarios, Maxent modeling was utilized to evaluate the distribution and habitat of P. veitchii in China. The aims of this research were to: (1) determine the key factors influencing its distribution; (2) predicting its potential distribution under current and future (2050s and 2070s) climate scenarios by incorporating the topographic and bioclimatic data; and, (3) projecting and quantifying the suitable habitat shift under 2050s and 2070s climate scenarios. The results will offer a theoretical basis and reference for protecting, introducing, and cultivating wild P. veitchii resources. 2. Materials and Methods A flowchart was prepared to summarize the full workflow and it serves as the basis of the analyses (Figure1). In brief, first the data were summarized and environmental variables were selected. Next, Maxent modeling was completed, followed by predicting the current and future conditions. Lastly, the resulting data comprehensively evaluated. Forests 2019, 10, x FOR PEER REVIEW 3 of 13 95 10 km × 10 km grid cell. A total of 212 known occurrences of P. veitchii were documented while using 96 ArcGIS 10.2 (Esri, Redlands, CA, USA) (Figure 2). 97 2.2. Environmental Variables 98 Thirty environmental variables were chosen that might affect the distribution of P. veitchii. 99 Those included 19 bioclimatic variables with 30 s spatial resolution (also referred to as 1 km2 spatial 100 resolution) obtained from the World Climate Database (www.worldclim.org) [25]. In addition, 101 elevation (ELE) data were obtained from the international scientific data service platform of the 102 Chinese Academy of Sciences (http://datemirro.csdb.cn), while growing degree days (GDD), soil pH 103 (SpH), and soil organic carbon (SC) data from the Center for Sustainability and the Global 104 Environment (http://www.sage.wisc.edu/atlas/ index.php) [26]. Ground-frost frequency (FRS), 105 wet-day frequency (WET), and vapor pressure (VAP) were obtained from the IPCC database 106 (http://www.sage.wisc.edu /atlas/index.php) and Global ultraviolet-B radiation (UVB 1–4) from the 107 gIUV database (http://www.ufz.de/gliv/) [27]. These environmental variables were transferred into 108 ASCII format by using ArcGIS Conversion Tools and were then used to overlap with a 1: 4,000,000 109 scale map of China that was obtained from the National Fundamental Geographic Information 110 System website (http://nfgis.nsdi. gov.cn/) and used to extract environmental data. 111 Representative concentration pathways (RCPs) were defined in the 5th Assessment Report of 112 the IPCC in 2014. They defined four RCPs as the possible trajectories for greenhouse gas emissions 113 [28]. Climate modeling often uses these pathways to describe four possible climates, which are 114 defined as dependent on the volume of the greenhouse gas emissions in the near future worldwide 115 [29]. The four RCPs, including RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, are named after the potential 116 radiative forcing value in 2100 relative to the pre-industrial values (+2.6, +4.5, +6.0, and +8.5 W/m2, 117 respectively) [30]. The present study employed four climate change year/RCP-scenario combinations 118 from BCC-CSM1.1 climate change modeling data, where 2050 and 2070 use average emissions for 119 the years 2041 to 2060 and 2061 to 2080, respectively. RCP 2.6–2050, RCP 2.6–2070, RCP 8.5–2050, and 120 RCP 8.5–2070 are the four combinations. BCC-CSM1.1 serves as one of the most widely used models 121 for the simulation of the response of global climate to increased greenhouse gas emissions [31].